Design by exmple: An application of Armstrong relations
Journal of Computer and System Sciences
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
A database perspective on knowledge discovery
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Data surveyor: the nuggets in parallel
Advances in knowledge discovery and data mining
Selecting and reporting what is interesting
Advances in knowledge discovery and data mining
Inductive databases and condensed representations for data mining (extended abstract)
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
SQL+D: extended display capabilities for multimedia database queries
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Models and Lanuages of Object-Oriented Databases
Models and Lanuages of Object-Oriented Databases
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
A Tightly-Coupled Architecture for Data Mining
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Text mining documents in electronic data interchange environment
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Using text mining techniques in electronic data interchange environment
WSEAS Transactions on Computers
| Hi-index | 0.00 |
KDD is a rapidly expanding field with promise for great applicability. Knowledge discovery became the new database technology for the incoming years. The need for automated discovery tools caused an explosion in the number and type of tools available commercially and in the public domain. These requirements encouraged us to propose a new KDD model so called ODBC_KDD(2) described in [39]. "One of the ODBC_KDD(2) model requirements is the implementation of a query language that could handle DM rules"[40]. This query language called Knowledge Discovery Query Language (KDQL). KDQL is a companion of two major tasks in KDD such as DM and Data Visualization. These requirements motivates us to think for the possibility of joining the two tasks of KDD commonly known as Data Mining (DM) and Data Visualization (DV) together in one single KDD process. Integrating DM and DV requires a new database concept. This database concept is called "i-extended database". I-extended database will be retrieved by the use of KDQL. This I-extended database described in details in [42]. KDQL RULES operations were also theoretically proposed in this paper and some examples were given as well. KDQL RULES are used only to find out the association rules in i-extended database we have. The development and results of this paper would contribute to the data mining and visualization fields in several ways. The formulation of a set of heuristics for algorithms selection will help to clarify the matching between a specific problem and the set of best-suited algorithms or techniques (i.e. association rules) for solving it. These guidelines are expected to be useful and applicable to real DM projects.